Model comparison
GLM-5.2 vs Llama 3.2 1B
GLM-5.2 is the stronger model overall, scoring 51.1 to 20.1 on the Noometry Index. Llama 3.2 1B costs 30× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.
Last verified . 18 shared benchmarks.
MetaSummary
- They share 18 benchmarks with published results for both. GLM-5.2 scores higher in 9 categories and Llama 3.2 1B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5.2 leads 57.1 to 7.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 86.4% for GLM-5.2 and 0.6% for Llama 3.2 1B.
- Llama 3.2 1B is cheaper at $0.027 / $0.20 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
- GLM-5.2 accepts more context: 1M tokens versus 60K.
Side by side
| GLM-5.2 | Llama 3.2 1B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 51.1 | 20.1 |
| Released | 2026-06-13 | 2024-09-24 |
| Weights | Open | Open |
| Context window | 1M | 60K |
| Max output | 131K | 54K |
| Input $ / M tokens | $1.40 | $0.027 |
| Output $ / M tokens | $4.40 | $0.20 |
| Results tracked | 51 | 22 |
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Category by category
Coding GLM-5.2 leads
GLM-5.2: 51.3 (#41), Llama 3.2 1B: 21.1 (#338)
| Benchmark | GLM-5.2 | Llama 3.2 1B |
|---|---|---|
| LMArena Coding | 1485 | 1070 |
| SWE-bench Verified | 78.7% | — |
| DeepSWE | 43.8% | — |
| FrontierCode | 24.5% | — |
| LMArena WebDev | 1603 | — |
| SciCode | 50.5% | — |
| WeirdML | 70.1% | — |
| BigCodeBench Instruct | — | 8.2% |
| BigCodeBench Complete | — | 11.3% |
| ALE-Bench | 1,047 | — |
Agentic & Tool Use GLM-5.2 leads
GLM-5.2: 32.4 (#63), Llama 3.2 1B: 14.6 (#150)
| Benchmark | GLM-5.2 | Llama 3.2 1B |
|---|---|---|
| APEX-Agents | 45.2% | — |
| Berkeley Function Calling Leaderboard | — | 10.8% |
| τ²-bench Banking | 37.1% | — |
| PostTrainBench | 31.7% | — |
| BALROG | — | 6.6% |
| GBAEval | 0% | — |
| Vending-Bench 2 | 8,314 | — |
Reasoning GLM-5.2 leads
GLM-5.2: 42.3 (#52), Llama 3.2 1B: 16.2 (#308)
| Benchmark | GLM-5.2 | Llama 3.2 1B |
|---|---|---|
| Chess Puzzles | 21% | 0% |
| LMArena Hard Prompts | 1480 | 1044 |
| Epoch Capabilities Index | 151.78 | 101.99 |
| ARC-AGI-2 | 22.8% | — |
| SimpleBench | 58.8% | — |
| Kagi LLM Benchmark | 62.6% | — |
| NYT Connections (extended) | 74.3% | — |
| ARC-AGI-1 | 77% | — |
| CritPt | 20.9% | — |
| EBR-Bench | 9.5% | — |
| Mystery Game Puzzles | 19% | — |
| DTBench | 93.6% | — |
| LMCA | 45.8% | — |
| Surface Evolver Bench | 55.6% | — |
Math GLM-5.2 leads
GLM-5.2: 55.7 (#43), Llama 3.2 1B: 10.4 (#313)
| Benchmark | GLM-5.2 | Llama 3.2 1B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 86.4% | 0.6% |
| LMArena Math | 1482 | 1086 |
| FrontierMath (Tiers 1-3) | 59.2% | — |
| FrontierMath Tier 4 | 29.3% | — |
| MathArena Final-Answer Competitions | 67.6% | — |
| ProofBench | 35% | — |
Knowledge GLM-5.2 leads
GLM-5.2: 57.1 (#40), Llama 3.2 1B: 7.2 (#312)
| Benchmark | GLM-5.2 | Llama 3.2 1B |
|---|---|---|
| GPQA Diamond | 91.9% | 23.9% |
| LMArena Expert | 1486 | 1007 |
| SimpleQA Verified | 34.2% | — |
Multilingual GLM-5.2 leads
GLM-5.2: 55.8 (#26), Llama 3.2 1B: 23.8 (#292)
| Benchmark | GLM-5.2 | Llama 3.2 1B |
|---|---|---|
| LMArena Non-English | 1459 | 973 |
| LMArena Chinese | 1519 | 959 |
| LMArena German | 1468 | 1014 |
| LMArena Russian | 1466 | 941 |
| LMArena French | 1479 | — |
| LMArena Japanese | 1451 | — |
| LMArena Korean | 1445 | — |
| LMArena Spanish | 1477 | — |
Instruction Following GLM-5.2 leads
GLM-5.2: 76.9 (#34), Llama 3.2 1B: 52.4 (#290)
| Benchmark | GLM-5.2 | Llama 3.2 1B |
|---|---|---|
| LMArena Instruction Following | 1465 | 1031 |
Long Context GLM-5.2 leads
GLM-5.2: 45.3 (#43), Llama 3.2 1B: 31.9 (#274)
| Benchmark | GLM-5.2 | Llama 3.2 1B |
|---|---|---|
| LMArena Longer Query | 1479 | 1050 |
Writing & Preference GLM-5.2 leads
GLM-5.2: 70.4 (#21), Llama 3.2 1B: 21.3 (#310)
| Benchmark | GLM-5.2 | Llama 3.2 1B |
|---|---|---|
| LMArena Text | 1470 | 1055 |
| LMArena Creative Writing | 1462 | 1033 |
| EQ-Bench Creative Writing | 1757 | 200 |
| LMArena Multi-Turn | 1469 | 1030 |
| EQ-Bench 4 | 1222 | — |
Frequently asked questions
Is GLM-5.2 better than Llama 3.2 1B?
GLM-5.2 is the stronger model overall, scoring 51.1 to 20.1 on the Noometry Index. Llama 3.2 1B costs 30× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.
Which is cheaper, GLM-5.2 or Llama 3.2 1B?
Llama 3.2 1B is cheaper. It lists at $0.027 per million input tokens and $0.20 per million output tokens; GLM-5.2 lists at $1.40 and $4.40.
Is GLM-5.2 or Llama 3.2 1B better for coding?
GLM-5.2 scores higher on coding benchmarks: 51.3 versus 21.1 in the Noometry coding category.
Which has the bigger context window?
GLM-5.2 does, with 1M tokens against 60K.
How many benchmarks do GLM-5.2 and Llama 3.2 1B share?
18 benchmarks have published results for both models. GLM-5.2 has 51 scored results on Noometry and Llama 3.2 1B has 22.